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A new feature descriptor is presented for object and scene recognition. The new approach, called CDIKP, uniquely combines the scale-invariant feature detection with a robust projection kernel technique to produce highly efficient feature representation. The produced feature descriptors are highly-compact in comparisons to the state-of-the-art, do not require any pretraining step, and show superior...
In this paper, a novel multi-cue collaborative kernel tracking algorithm is proposed. A new constraint based on the property of cross ratio invariant enables tracking of objects insensitive to complex motions, including scale changes, rotation and especially views changes, without labeling and training. Meanwhile, invariant moments are introduced into the kernel based tracking method as the shape...
In content based image retrieval, the success of any distance-based indexing scheme depends critically on the quality of the chosen distance metric. We propose in this paper a kernel-based similarity approach working on sets of vectors to represent images. We introduce a method for fast approximate similarity search in large image databases with our kernel-based similarity metric. We evaluate our...
Traditional face superresolution methods treat face images as 1D vectors and apply PCA on the set of these 1D vectors to learn the face subspace. Zhang et al [7] proposed Two-directional two-dimensional PCA (2D)2-PCA for efficient face representation and recognition where images are treated as matrices instead of vectors. In this paper, we present a two-step algorithm for face superresolution. In...
Registration of 3D surfaces is a critical step for shape analysis. Recent studies show that spectral representations based on intrinsic pairwise geodesic distances between points on surfaces are effective for registration and alignment due to their invariance under rigid transformations and articulations. Kernel functions are often applied to the pairwise geodesic distances to make the registration...
In this paper, we propose volume based local Gabor binary patterns (V-LGBP) for face representation and recognition. In our method, the Gabor feature set of each gray image is regarded as a three dimensional ldquovolumerdquo, where the first two dimensions are spatial domain and the third dimension is the Gabor filter index. Then, the neighborhood order relationship in the ldquovolumerdquo is encoded...
In computer vision, background subtraction method is widely used to extract a changing region in a scene. However, it is difficult to simply apply this method to a scene with moving background object, because such object may be extracted as a changing region. Therefore, a method has been proposed to estimate both current background image and occluding object region simultaneously by using eigenspace-based...
We present a classifier unifying local features based representation and subspace based learning. We also propose a novel method to merge kernel eigen spaces (KES) in feature space. Subspace methods have traditionally been used with the full appearance of the image. Recently local features based bag-of-features (BoF) representation has performed impressively on classification tasks. We use KES with...
We present a hierarchical feature fusion model for image classification that is constructed by an evolutionary learning algorithm. The model has the ability to combine local patches whose location, width and height are automatically determined during learning. The representational framework takes the form of a two-level hierarchy which combines feature fusion and decision fusion into a unified model...
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